We meta-train separate models on the self-supervised task distributions, without any access to the downstream supervised tasks.
The models are then fine-tuned on the downstream task training sets which consist of \(k=8,16,32\)  examples per class. Note that tasks can have different number of classes.
Following [1]}, we use the development set of Scitail and Amazon-Electronics to select the number of steps of fine-tuning for all models, all other hyper-parameters are kept the same as meta-training.
Since few-shot performance is sensitive to the few examples in training, each model is fine-tuned on 10 sets for each task and the average test performance is reported with standard deviation.
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